The article discusses the role of big data (BD) in pharmacology, toxicology, and pharmaceutics, but there are significant issues that require attention. First, the paper makes broad claims about BD’s potential benefits in drug discovery, safety, and personalized medicine without providing sufficient empirical evidence or real-world case studies. The references to machine learning and other BD applications are not well integrated into the discussion, leaving the claims unsubstantiated and somewhat abstract.
Additionally, the figures, particularly Figure 1, lack clarity in illustrating the specific steps involved in the BD workflow. The connection between data cleaning, transformation, and analysis is not explicitly laid out, which makes the figure less helpful in understanding the data flow. Table 1 is similarly vague, listing broad applications without offering concrete examples or case studies that could demonstrate the practical implementation of BD in these fields.
The limitations section is also insufficient, especially regarding the challenges in data quality, integration, and privacy. These issues are critical when discussing BD in healthcare and drug development but are not adequately explored. Addressing these gaps with specific examples and more rigorous evidence would significantly strengthen the paper. A corrigendum is recommended to clarify these major issues and improve the paper’s overall impact.
I hope that the authors will find these remarks helpful.